igital twinning technology originated in the field of aerospace. The real-time and bidirec-tional feature of data interaction guarantees its advantages of high accuracy, real-time performance and scalability. In this paper the digital twin technology was introduced to electric vehicle energy consumption research. First, an energy consumption model of an electric vehicle of BAIC BJEV was established, then the model was optimized and verified through the energy consumption data of the drum test. Based on the data of the vehicle real-time monitoring platform, a digital twin model was built, and it was trained and updated by daily new data. Eventually it can be used to predict and verify the data of vehicle. In this way the prediction of energy consumption of vehicles can be achieved.

The Internet of Things (IoT) is revolutionising how energy is delivered from energy producers and used throughout residential households. Optimising the residential energy consump-tion is a crucial step toward having greener and sustainable energy production. Such optimisation requires a household-centric energy management system as opposed to a one-rule-fits all approach. In this paper, we propose a data-driven multi-layer digital twin of the energy system that aims to mirror households’ actual energy consumption in the form of a household digital twin (HDT). When linked to the energy production digital twin (EDT), HDT empowers the household-centric energy optimisation model to achieve the desired efficiency in energy use. The model intends to improve the efficiency of energy production by flattening the daily energy demand levels. This is done by collaboratively reorganising the energy consumption patterns of residential homes to avoid peak demands whilst accommodating the resident needs and reducing their energy costs. Indeed, our system incorporates the first HDT model to gauge the impact of various modifications on the household energy bill and, subsequently, on energy production. The proposed energy system is applied to a real-world IoT dataset that spans over two years and covers seventeen households. Our conducted experiments show that the model effectively flattened the collective energy demand by 20.9% on synthetic data and 20.4% on a real dataset. At the same time, the average energy cost per household was reduced by 10.7% for the synthetic data and 17.7% for the real dataset.